It is well understood within trade industry that products manufactured/shipped out-of-season can have a higher risk of being substandard and/or deteriorating. As a well-known example, apples arriving from Australia in the month of November may be suspect, due to the seasonality of apple harvest in Australia. Similarly, mangoes arriving from California in the month of February may be suspect, due to the seasonality of mango harvest in California. Most of the vegetables, food crops, and fruits show seasonality trends, which can be analyzed for anomaly detection. Industrial products also show seasonality trends, although to a lesser extent.
There are notable and understandable exceptions to seasonality rules. For example, some companies in the US have excellent greenhouse operations, and as part of their business model, they ship some tomatoes in winter, to cater to the winter demand of tomatoes.
References for related art include:    1. “Algorithms for Mining Distance-Based Outliers in Large Datasets”, Edwin M. Knox and Raymond T. Ng, Department of Computer Science, University of British Columbia, Vancouver, BC V6T 124 Canada.    2. “Applications of data mining in computer security”, by Daniel Barbará, Sushil Jajodia, Kluwer Academic Publishers, 2002.    3. “Seasonal outliers in time series”, Regina Kaiser and Agustin Maravall, Banco de España Working Papers, 1999.    4. “Distance-based outliers: algorithms and applications”, Edwin M. Knorr, Raymond T. Ng and Vladimir Tucakov, The VLDB Journal, Springer Berlin/Heidelberg, Volume 8, Numbers 3-4/February, 2000.    5. Distance Based Outlier for Data Streams Using Grid Structure, Manzoor Elahi, Lv Xinjie, M. Wasif Nisar and Hongan Wang, Information Technology Journal, 2009, Volume: 8, Issue: 2, Page No.: 128-137.    6. Multiple hierarchical classification of free-text clinical guidelines, Robert Moskovitch, Shiva Cohen-Kashi, Uzi Dror, Iftah Levy, Amit Maimon and Yuval Shahar, Medical Informatics Research Center, Department of Information Systems Engineering, Ben Gurion University, P.O. Box 653, Beer Sheva 84105, Israel.    7. “Greenhouse Tomatoes Change the Dynamics of the North American Fresh Tomato Industry”, at http://postharvest.ucdavis.edu/datastorefiles/234-447.pdf.    8. “Methods for estimating the seasonality of groups of similar items”, http://www.patentstorm.us/patents/6834266.html.    9. “Decision support system for the management of an agile supply chain”, http://www.patentstorm.us/patents/6151582.html.    10. “System and method for detecting traffic anomalies”, http://www.patentstorm.us/patents/6177885/description.html.    11. “Anomaly detection system and a method of teaching it”, http://www.freepatentsonline.com/7613668.html.
However, the invention and embodiments described here, below, have not been addressed or presented, in any prior art.